KR20190029764A - Optical measurement of aperture dimensions in a wafer - Google Patents

Abstract

A method of generating 3D information of a sample using an optical microscope includes the steps of: varying the distance between the sample and the objective lens of the optical microscope in predetermined steps; Capturing an image at each predetermined step; Determining a property value of each pixel in each captured image; Determining a first captured image to be focused on a first surface of the sample based on a property value of each pixel in each captured image; And determining a measure of an aperture of the first surface of the sample based on the first captured image. The first surface of the sample and the second surface of the sample are within the field of view of each captured image. The first captured image includes a pattern overlay. In another example, aperture measurements are determined using a second captured image without a pattern overlay.

Description

Optical measurement of aperture dimensions in a wafer

Cross reference to related applications

This application is a continuation-in-part of U.S. Serial No. 15 / 233,812 entitled " AUTOMATED 3-D MEASUREMENT ", filed August 10, 2016, 120 claims priority. The disclosure of which is incorporated herein by reference in its entirety.

Technical field

The described embodiments are generally directed to measuring 3-D information of a sample and more specifically to automatically measuring 3-D information in a fast and reliable manner.

Three-dimensional (3-D) measurements of various objects or samples are useful in many different applications. One such application is in wafer level package processing. The 3-D measurement information of the wafer during different stages of wafer level fabrication can provide insight into the presence of wafer processing defects that may be present on the wafer. The 3-D measurement information of the wafer during wafer level fabrication can provide insight into the absence of defects before additional capital is consumed to continue wafer processing. The 3-D measurement information of the sample is collected by manipulating the microscope at present. The human user focuses the microscope using the eye to determine when the microscope is focused on the surface of the sample. An improved method of collecting 3-D measurement information is needed.

In a first novel aspect, the three-dimensional (3-D) information of a sample is generated by varying the distance between the sample and the objective lens of the optical microscope in predetermined steps, using an optical microscope. The images are captured at each predetermined step. The first surface of the sample and the second surface of the sample are within the field of view of each captured image. The characteristic value of each pixel in each captured image is determined. A first captured image focused on a first surface of the sample is determined based on a characteristic value of each pixel in each captured image. A measure of the aperture of the first surface of the sample is determined based on the first captured image.

In a second novel aspect, a three-dimensional (3-D) measurement system includes an optical microscope comprising an objective lens and a stage, and a computer system comprising a processor and a storage device, Wherein the computer system is configured to: (i) store an image captured at each predetermined step; and (iii) change the distance between the first surface and the second surface of the sample, A second surface of the sample is captured in each image; (ii) determining a characteristic of each pixel in each captured image; (iii) determining a first captured image to be focused on a first surface of the sample based on a characteristic of each pixel in each captured image; (iv) determine a measure of the aperture of the first surface of the sample based on the first captured image.

In a third novel aspect, the three-dimensional (3-D) information of the sample may be obtained by using an optical microscope to: (i) vary the distance between the sample and the objective lens of the optical microscope in predetermined steps; (ii) capturing an image at each predetermined step, wherein a pattern is projected onto the sample while each image is captured, and wherein a first surface of the sample and a second surface of the sample are projected onto each captured image - within the field of vision; (iii) determining a property value of each pixel in each captured image; (iv) determining a first captured image that is focused on a first surface of the sample based on a property value of each pixel in each captured image, the first captured image being focused on a focal distance ; (v) capturing a second image taken at the focal distance, the pattern not being projected onto the sample while the second image is captured; And (vi) determining a measure of an aperture of the first surface of the sample based on the second captured image.

Additional details and embodiments and techniques are set forth in the following description. This summary does not imply defining the invention. The invention is defined by the claims.

BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, wherein like numerals represent like components, illustrate embodiments of the invention.
1 is a diagram of a semi-automatic 3-D measurement system 1 for performing automated 3-D measurements of a sample.
Figure 2 is a view of a 3-D imaging microscope 10 that includes adjustable objectives 11 and an adjustable stage 12.
3 is a diagram of a 3-D metrology system 20 including a 3-D microscope, sample handler, computer, display, and input device.
4 is a view showing a method of capturing an image as the distance between the objective lens of the optical microscope and the stage changes.
5 is a chart showing the distance between the objective lens of the optical microscope and the sample surface whose xy coordinate has the maximum characteristic value.
FIG. 6 is a 3-D diagram of an image rendered using the maximum characteristic values for each xy coordinate shown in FIG.
7 is a diagram illustrating peak mode operation using captured images at various distances.
8 is a diagram illustrating peak mode operation using images captured at various distances when the photoresist apertures are in the field of view of an optical microscope.
Figure 9 is a chart showing 3-D information resulting from peak mode operation.
10 is a diagram illustrating a summation mode operation using images captured at various distances.
Figure 11 is a diagram illustrating erroneous surface detection when using summing mode operation.
12 is a chart showing 3-D information resulting from summing mode operation.
13 is a diagram illustrating range mode operation using captured images at various distances.
14 is a chart showing 3-D information from range mode operation.
15 is a chart showing only counts of pixels having characteristic values in the first range.
16 is a chart showing only counts of pixels having characteristic values in the second range.
Figure 17 is a flow chart illustrating various steps involved in peak mode operation.
18 is a flow chart illustrating various steps involved in range mode operation.
19 is a diagram of a captured image including a single feature focused on the top surface of the photoresist layer.
Figure 20 is a diagram illustrating a first method for generating an intensity threshold.
21 is a diagram illustrating a second method of generating a strength threshold;
22 is a diagram illustrating a third method of generating an intensity threshold.
23 is a 3-D diagram of a photoresist opening in a sample.
24 is a 2D view of the top surface opening of the photoresist shown in FIG. 23. FIG.
25 is a 2D view of the bottom surface opening of the photoresist shown in Fig. 23. Fig.
Figure 26 is a captured image focused on the top surface of the photoresist layer.
27 is a view for explaining the detection of the boundary of the photoresist layer shown in Fig. 26.
28 is a captured image focused on the lower surface of the photoresist layer.
29 is a diagram showing the detection of the boundary of the photoresist layer shown in Fig.
Figure 30 is a captured image focused on the top surface of the photoresist layer of the trench structure.
31 is a view for explaining the detection of the boundary of the photoresist layer shown in FIG. 30. FIG.

Reference will now be made in detail to background examples and embodiments of the present invention, examples of which are illustrated in the accompanying drawings. In the following description and in the claims, the terms "top", "down", "upper", "lower", "top" Relational terms such as " lfet "and" right "can be used to describe the relative orientation between different parts of the structure being described, Lt; RTI ID = 0.0 > direction. ≪ / RTI >

Figure 1 is a diagram of a semi-automated 3-D metrology system 1. The semi-automatic 3-D measuring system 1 includes an optical microscope (not shown), an ON / OFF button 5, a computer 4 and a stage 2. In operation, the wafer 3 is placed on the stage 2. The function of the semi-automatic 3-D metrology system 1 is to capture multiple images of an object and generate 3-D information that automatically describes the various surfaces of the object. It is also referred to as the "scan" of an object. The wafer 3 is an example of an object that is analyzed by the semi-automatic 3-D measurement system 1. An object is also referred to as a sample. In operation, the wafer 3 is placed on the stage 2, and the semi-automatic 3-D metrology system 1 starts the process of automatically generating 3-D information describing the surface of the wafer 3 . In one example, the semi-automatic 3-D measurement system 1 begins by pressing a designated key on a keyboard (not shown) connected to the computer 4. [ In another example, the semi-automatic 3-D metrology system 1 begins by sending a start command to the computer 4 over a network (not shown). The semi-automatic 3-D metrology system 1 may also be configured to mate with an automated wafer handling system (not shown) that automatically removes the wafers once the wafers have been scanned and inserts new wafers for scanning have.

A fully automated 3-D metrology system (not shown) is similar to the semiautomatic 3-D metrology system of FIG. 1; However, a fully automated 3-D measurement system also includes a robot handler that can automatically pick up wafers and place wafers on the stage without human intervention. In a similar manner, a fully automated 3-D metrology system can also automatically pick up wafers from the stage and remove wafers from the stage using a robot handler. A fully automated 3-D metrology system is desirable during many wafer production because it avoids the possibility of contamination by human operators and improves time efficiency and overall cost. Alternatively, the semi-automatic 3-D metrology system 1 is desirable during research and development activities when only a small number of wafers need to be measured.

2 is a diagram of a 3-D imaging microscope 10 including a plurality of objective lenses 11 and an adjustable stage 12. In Fig. The 3-D imaging microscope may be a confocal microscope, a structured illumination microscope, an interferometric microscope, or any other type of microscope known in the art. The confocal microscope will measure the intensity. The structured illumination microscope will measure the contrast of the projected structure. The interferometer microscope will measure the interference fringe contrast.

In operation, the wafer is placed on the adjustable stage 12 and the objective lens is selected. The 3-D imaging microscope 10 captures multiple images of the wafer when the height of the stage on which the wafer lies is adjusted. This results in multiple images of the wafer being captured while the wafer is positioned at various distances from the selected lens. In one alternative example, the wafer is placed on a fixed stage and the position of the objective lens is adjusted to change the distance between the objective lens and the sample without moving the stage. In another example, the stage is adjustable in the x-y direction and the objective lens is adjustable in the z-direction.

The captured image may be stored locally in the memory included in the 3-D imaging microscope 10. Alternatively, the captured image may be stored in a data storage device included in the computer system, and the 3-D microscope 10 transmits the captured image to the computer system via a data communication link. Examples of data communication links include wireless networks such as Universal Serial Bus (USB) interfaces, Ethernet connections, FireWire bus interfaces, and WiFi.

3 shows a diagram of a 3-D metrology system 20 including a 3-D microscope 21, a sample handler 22, a computer 23, a display 27 (optional) and input devices 28 to be. The 3-D measuring system 20 is an example of a system included in the semi-automatic 3-D measuring system 1. The computer 23 includes a processor 24, a storage device 25, and a network device 26 (optional). The computer outputs information to the user via the display 27. The display 27 can be used as an input device even when the display is a touch screen device. The input device 28 may include a keyboard and a mouse. The computer 23 controls the operation of the 3-D microscope 21 and the sample handler / stage 22. When a start scan command is received by the computer 23, the computer sends one or more commands ("scope control data") to configure a 3-D microscope for image capture do. For example, the correct objective needs to be selected, the resolution of the image to be captured needs to be selected, and a mode for storing the captured image needs to be selected. When the scan start command is received by the computer 23, the computer sends one or more commands ("handler control data") to configure the sample handler / For example, an exact height (z-direction) adjustment needs to be selected and an exact horizontal (x-y dimension) alignment needs to be selected.

During operation, the computer 23 causes the sample handler / stage 22 to be adjusted to the proper position. Once the sample handler / stage 22 is properly positioned, the computer 23 will cause the 3-D microscope to focus on the focal plane to capture at least one image. Then, the computer 23 will cause the stage to move in the z-direction so that the distance between the sample and the objective lens of the optical microscope is changed. When the stage is moved to a new location, the computer 23 will cause the optical microscope to capture the second image. This process continues until an image is captured at each desired distance between the objective lens of the optical microscope and the sample. The captured image ("image data") at each distance is transferred from the 3-D microscope 21 to the computer 23. The captured image is stored in the storage device 25 included in the computer 23. [ In one example, the computer 23 analyzes the captured image and outputs 3-D information to the display 27. In another example, the computer 23 analyzes the captured image and outputs the 3-D information to the remote device via the network 29. In another example, the computer 23 does not analyze the captured image, but instead transmits the captured image to another device over the network 29 for processing. The 3-D information may include a rendered 3-D image based on the captured image. The 3-D information may not include any image, but may contain data based on various characteristics of each captured image.

4 is a view for explaining a method of capturing an image as the distance between the objective lens and the sample of the optical microscope changes. In the embodiment shown in Fig. 4, each image includes 1000 x 1000 pixels. In another embodiment, the image may comprise various configurations of pixels. In one example, the spacing between successive distances is fixed to a predetermined amount. In another example, the spacing between consecutive distances may not be fixed. Since there is no fixed spacing between images in the z-direction, it may be advantageous if an additional z-direction resolution is required for only a portion of the z-directional scan of the sample. Since the z-direction resolution is based on the number of images captured per unit length in the z-direction, capturing additional images per unit length in the z-direction increases the z-direction resolution measured. Conversely, capturing fewer images per unit length in the z-direction decreases the z-direction resolution being measured.

As described above, the optical microscope is first adjusted to focus on the focal plane positioned at the distance 1 from the objective lens of the optical microscope. The optical microscope then captures the image stored in the storage device (i.e., "memory"). Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample is a distance 2. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample is 3 degrees. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample becomes a distance 4. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample becomes distance 5. The optical microscope then captures the image stored in the storage device. This process continues for N different distances between the objective lens of the optical microscope and the sample. The information indicating which image is associated with each distance is also stored in the storage device for processing.

In an alternative embodiment, the distance between the objective lens of the optical microscope and the sample is fixed. Rather, the optical microscope includes a zoom lens which causes the optical microscope to change the focal plane of the optical microscope. In this way, the focal plane of the optical microscope is changed over N different focal planes while the sample supported by the stage and stage is fixed. An image is captured for each focal plane and stored in a storage device. The images captured over all the various focal planes are then processed to determine the 3-D information of the samples. This embodiment requires a zoom lens that can provide sufficient resolution over all focal planes and introduce minimal image distortion. Further, a correction between each zoom position and the corresponding focal length of the zoom lens is required.

5 is a chart showing the distance between the objective lens of the optical microscope and the sample having the maximum characteristic value of each x-y coordinate. By capturing and storing the image for each distance, you can analyze the characteristics of each pixel in each image. For example, the intensity of light of each pixel of each image can be analyzed. In another example, the contrast of each pixel of each image can be analyzed. In yet another example, the fringe contrast of each pixel of each image can be analyzed. The contrast of a pixel can be determined by comparing the intensity of the pixel to the intensity of a predetermined number of surrounding pixels. A further discussion of how to generate contrast information is provided in U.S. Patent Application No. 12 / 699,824 entitled " 3-D Optical Microscope "filed on February 3, 2010 by James Jianguo Xu et al. (The contents of which are incorporated herein by reference).

FIG. 6 is a 3-D diagram of a 3-D image rendered using the maximum characteristic values for each x-y coordinate shown in FIG. All pixels with an X position between 1 and 19 have a maximum characteristic value at z-direction distance 7. All pixels with an X position between 20 and 29 have a maximum characteristic value at z-direction distance 2. All pixels with X positions between 30 and 49 have the maximum characteristic value at z-direction distance 7. All pixels having an X position between 50 and 59 have a maximum characteristic value at z-direction distance 2. All pixels with an X position between 60 and 79 have a maximum characteristic value at z-direction distance 7. In this way, the 3-D image shown in FIG. 6 can be generated using the maximum characteristic value per x-y pixel over all the captured images. Further, when the distance 2 is known and the distance 7 is known, the depth of the well shown in FIG. 6 can be calculated by subtracting the distance 7 from the distance 2.

Peak mode operation (PEAK MODE OPERATION)

Figure 7 is a diagram illustrating peak mode operation using captured images at various distances. As discussed above with respect to FIG. 4, the optical microscope is first adjusted to focus on a plane located at distance 1, away from the objective lens of the optical microscope. The optical microscope then captures the image stored in the storage device (i.e., "memory"). Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample is a distance 2. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample is 3 degrees. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample becomes a distance of 4 degrees. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample becomes distance 5. The optical microscope then captures the image stored in the storage device. This process continues for N different distances between the objective lens of the optical microscope and the stage. The information indicating which image is associated with each distance is also stored in the storage device for processing.

Instead of determining a maximum characteristic value for each xy position over all captured images at various z-distances, a maximum characteristic value over all xy positions of a single captured image at one z-distance is determined in peak mode operation do. In other words, for each captured image, the maximum characteristic value over all pixels included in the captured image is selected. As shown in FIG. 7, the pixel position with the maximum feature value will vary between different captured images. The characteristic may be intensity, contrast or fringe contrast.

Figure 8 is a diagram illustrating peak mode operation using captured images at various distances when a photoresist (PR) aperture is within the field of view of an optical microscope. A top-down view of the object shows the cross-sectional area of the PR aperture in the x-y plane. The PR aperture also has a certain depth in the z-direction. The images captured at various distances are shown below the top-down view of FIG. At distance 1, the optical microscope is not focused on the upper surface of the wafer or the lower surface of the PR aperture. At distance 2, the optical microscope is focused on the lower surface of the PR aperture, but not on the upper surface of the wafer. This results in an increased characteristic value (intensity / contrast / fringe contrast) in the pixel receiving light that is reflected from the lower surface of the PR aperture, compared to a pixel that receives reflected light from another surface that is out of focus (the upper surface of the wafer) ). At distance 3, the optical microscope is not focused on the upper surface of the wafer or the lower surface of the PR aperture. Therefore, the maximum characteristic value at distance 3 will be substantially lower than the maximum characteristic value measured at distance 2. At distance 4, the optical microscope is not focused on any surface of the sample; However, due to the difference between the refractive index of air and the refractive index of the photoresist layer, an increase in the maximum characteristic value (intensity / contrast / fringe contrast) is measured. Figure 11 and the accompanying text illustrate this phenomenon in more detail. At distance 6, the optical microscope is focused on the upper surface of the wafer, but not on the lower surface of the PR aperture. (Intensity / contrast / fringe contrast) in pixels receiving light reflected from the upper surface of the wafer, compared to pixels receiving light reflected from another surface that is out of focus (the lower surface of the PR aperture) ≪ / RTI > Once the maximum characteristic value is determined from each captured image, the result can be used to determine how far the surface of the wafer is located.

9 is a chart showing 3-D information due to peak mode operation. As discussed with respect to FIG. 8, the maximum feature value of the image captured at distances 1, 3, and 5 has a lower maximum feature value compared to the maximum feature value of the image captured at distances 2, 4, and 6. The curve of the maximum characteristic value at various z-distances may include noise due to environmental influences such as vibration. In order to minimize such noise, a standard smoothing method such as Gaussian filtering with a specific kernel size can be applied before further data analysis.

One method of comparing the maximum characteristic values is performed by a peak finding algorithm. In one example, a derivative method is used to locate the zero crossing point along the z-axis to determine the distance that each "peak" is present. Then, the maximum characteristic value is compared at each distance at which the peak is found to determine the distance at which the largest characteristic value is measured. In the case of Figure 9, a peak at distance 2 will be found, which is used as an indication that the surface of the wafer is at distance 2.

Another method of comparing the maximum characteristic values is performed by comparing each maximum characteristic value with a preset threshold value. Threshold values can be calculated based on wafer material, distance, and optical microscope specifications. Alternatively, the threshold may be determined by empirical testing prior to automatic processing. In either case, the maximum characteristic value for each captured image is compared to a threshold value. If the maximum characteristic value is greater than the threshold value, it is determined that the maximum characteristic value represents the presence of the surface of the wafer. If the maximum characteristic value is not greater than the threshold value, it is determined that the maximum characteristic value does not represent the surface of the wafer.

SUMMATION MODE OPERATION

10 is a diagram illustrating aggregate mode operation using captured images at various distances. As discussed above with respect to FIG. 4, the optical microscope is first adjusted to focus on a plane located at distance 1, away from the objective lens of the optical microscope. The optical microscope then captures the image stored in the storage device (i.e., "memory"). Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample is a distance 2. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample is 3 degrees. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample becomes a distance 4. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample becomes distance 5. The optical microscope then captures the image stored in the storage device. This process continues for N different distances between the objective lens of the optical microscope and the sample. The information indicating which image is associated with each distance is also stored in the storage device for processing.

Instead of determining a maximum characteristic value over all x-y positions of a single captured image at a single z-distance, the characteristic values of all x-y positions of each captured image are added together. In other words, for each captured image, the property values for all pixels included in the captured image are summed together. The characteristic may be intensity, contrast or fringe contrast. A summed characteristic value that is substantially greater than the average summed characteristic value of adjacent z-distances indicates that the surface of the wafer is at a distance. However, this method may also result in the false positive described in FIG.

11 is a view for explaining erroneous surface detection when the summation mode operation is used. The wafer shown in FIG. 11 includes a silicon substrate 30 and a photoresist layer 31 deposited on top of the silicon substrate 30. The upper surface of the silicon substrate 30 is located at a distance 2. The upper surface of the photoresist layer 31 is located at a distance 6. The image captured at distance 2 will result in a summation of characteristic values substantially larger than other images captured at a distance where the surface of the wafer does not exist. An image captured at distance 6 will result in a summation of characteristic values substantially larger than other images captured at a distance where the surface of the wafer does not exist. At this point, summation mode operation appears to be a valid indicator of the presence of the wafer surface. However, an image captured at distance 4 will result in a summation of characteristic values that are substantially larger than other images captured at a distance where the surface of the wafer does not exist. This is a problem because the surface of the wafer is not located at a distance 4, as is clearly shown in Fig. Rather, the increase in the sum of the characteristic values at distance 4 is the artifact of the surfaces at distances 2 and 6. Most of the light irradiating the photoresist layer is not reflected but rather moves to the photoresist layer. The angle at which this light travels changes due to the difference in refractive index between air and photoresist. The new angle is closer to perpendicular than the angle of the light illuminating the top surface of the photoresist. Light travels to the upper surface of the silicon substrate below the photoresist layer. The light is then reflected by a highly reflected silicon substrate layer. The angle of the reflected light changes again when the reflected light leaves the photoresist layer and enters the air due to the difference in refractive index between the air and the photoresist layer. The redirection, reflection and second redirection of the illuminating light in this manner allows the optical microscope to observe an increase in characteristic values (intensity / contrast / fringe contrast) at a distance of 4. This example shows that whenever the sample contains a transparent material, the summation mode operation will detect a surface that is not present on the sample.

12 is a chart showing 3-D information resulting from summing mode operation. This chart shows the result of the phenomenon shown in Fig. A large value of the summed characteristic values at distance 4 incorrectly indicates the presence of the surface at distance 4. There is a need for a method that does not result in a positive error indication of the presence of the surface of the wafer.

Range mode operation (RANGE MODE OPERATION)

13 is a diagram illustrating range mode operation using captured images at various distances. As discussed above with respect to FIG. 4, the optical microscope is first adjusted to focus on a plane located at distance 1, away from the objective lens of the optical microscope. The optical microscope then captures the image stored in the storage device (i.e., "memory"). The stage is then adjusted so that the distance between the objective lens of the optical microscope and the sample is a distance 2. The optical microscope then captures the image stored in the storage device. The stage is then adjusted so that the distance between the objective lens of the optical microscope and the sample is a distance 3. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample becomes a distance 4. The optical microscope then captures the image stored in the storage device. Thereafter, the stage is adjusted so that the distance between the objective lens of the optical microscope and the sample becomes distance 5. The optical microscope then captures the image stored in the storage device. This process continues for N different distances between the objective lens of the optical microscope and the sample. The information indicating which image is associated with each distance is also stored in the storage device for processing.

Instead of determining the sum of all feature values over all xy positions of a single captured image at a single z-distance, a count of pixels with feature values within a specific range included in a single captured image is determined. In other words, for each captured image, a count of pixels having characteristic values within a certain range is determined. The characteristic may be intensity, contrast or fringe contrast. A count of pixels at one particular z-distance that is substantially greater than the average count of pixels at adjacent z-distances indicates that the surface of the wafer is at a distance. This method reduces the positive error described in Fig.

14 is a chart showing 3-D information from range mode operation. Given the different types of materials present on the wafer and knowledge of the optical microscopy configuration, an expected range of characteristic values can be determined for each material type. For example, the photoresist layer will reflect a relatively small amount (i.e., 4%) of the light that illuminates the top surface of the photoresist layer. The silicon layer will reflect light (i.e., 37%) that illuminates the top surface of the silicon layer. The redirected reflection (i.e., 21%) observed at distance 4 would be substantially greater than the reflection observed at distance 6 from the top surface of the photoresist layer; However, the redirected reflection (i.e., 21%) observed at distance 4 would be substantially less than the reflection observed at distance 2 from the top surface of the silicon substrate. Thus, when looking for the top surface of the photoresist layer, a first range centered on the expected characteristic value for the photoresist can be used to filter out pixels having characteristic values out of the first range, Thereby filtering out pixels having characteristic values not attributable to reflection from the upper surface of the photoresist layer. The pixel count over all distances generated by applying the property values of the first range is shown in Fig. As shown in FIG. 15, some pixels (not necessarily all pixels) from other distances (surfaces) are filtered out by applying a first range. This occurs when characteristic values measured at various distances fall within the first range. Nonetheless, the application of the first range before counting pixels still functions to make the pixel count at the desired surface more noticeable by comparing it to other pixel counts at different distances. This is shown in FIG. The pixel count at distance 6 is greater than the pixel count at distances 2 and 4 after the first range is applied, but the pixel count at distance 6 before the first range is applied (as shown in FIG. 14) And 4 pixel counts.

In a similar manner, when looking at the top surface of the silicon substrate layer, a second range centered on expected property values for the silicon substrate layer may be used to filter out pixels having characteristic values outside the second range, And filters out pixels having characteristic values not due to reflection from the upper surface of the silicon substrate layer. A pixel count over all distances generated by applying the property values of the second range is shown in FIG. This application of the range reduces the misrepresentation of the wafer surface located at distance 4 by knowing what property values are expected from all the materials present on the wafer being scanned. As discussed with respect to FIG. 15, some pixels (not necessarily all pixels) from another distance (surface) are filtered out by applying a range. However, when the characteristic values measured at several distances are not within the same range, the result of applying the range will remove all pixel counts from the other distance (surface). Figure 16 illustrates this scenario. In Fig. 16, a second range is applied before generating the pixel count at each distance. As a result of applying the second range, only pixels of distance 2 are counted. This creates a very clear indication that the surface of the silicon substrate is at distance 2.

Note that a standard smoothing operation such as Gaussian filtering may be applied to the entire pixel count along the z-distance prior to performing any peak search operation, by reducing the impact caused by potential noise such as environmental vibrations.

FIG. 17 is a flow diagram 200 illustrating various steps involved in peak mode operation. In step 201, the distance between the sample and the objective lens of the optical microscope is changed at a predetermined step. At step 202, an image is captured at each predetermined step. In step 203, the characteristics of each pixel in each captured image are determined. In step 204, for each captured image, the maximum characteristic across all pixels in the captured image is determined. At step 205, the largest property for each captured image is compared to determine whether the surface of the sample is present at each predetermined step.

18 is a flow diagram 300 illustrating various steps involved in range mode operation. In step 301, the distance between the sample and the objective lens of the optical microscope is changed in a predetermined step. At step 302, an image is captured at each predetermined step. At step 303, the characteristics of each pixel in each captured image are determined. In step 304, for each captured image, a count of pixels having characteristic values in the first range is determined. In step 305, it is determined whether the surface of the sample is present at each predetermined step based on a count of pixels for each captured image.

19 is a diagram of a captured image including a single feature. One example of a feature is an opening in the shape of a circle in the photoresist layer. Another example of a feature is a trench-like opening in a photoresist layer, such as an unplated redistribution line (RDL) structure. During the wafer fabrication process, the ability to measure various features of photoresist openings in the wafer layer is advantageous. The measurement of the photoresist opening allows the defect of the structure to be detected before the metal is plated into the hole. For example, if the size of the photoresist opening is not correct, the plated RDL width will be incorrect. Detection of this type of defect can prevent further fabrication of defective wafers. By preventing further fabrication of defective wafers, material and process costs can be reduced. Figure 19 shows that the measured intensity of light reflected from the top surface of the photoresist layer is greater than the measured intensity of light reflected from the aperture of the photoresist layer when the captured image is focused on the top surface of the photoresist layer. As discussed in more detail below, information associated with each pixel of the captured image can be used to generate intensity values for each pixel of the captured image. The intensity value of each pixel is then compared to the intensity threshold to determine whether each pixel is associated with a first region of the captured image, such as the top surface of the photoresist layer, or a second region of the captured image, Region of interest. This is done by (i) first applying an intensity threshold to the measured intensity of each pixel of the captured image, and (ii) categorizing all pixels having intensity values below the intensity threshold as being associated with the first region of the captured image , (iii) categorizing all pixels having intensity values above the intensity threshold as being associated with a second region of the captured image, and (iv) defining the feature as a group of pixels within the same region contiguous with other pixels associated with the same region .

The captured image shown in Fig. 19 may be a color image. Each pixel of the color image includes red, blue, and green (RBG) channel values. Each of these color values may be combined to produce a single intensity value for each pixel. Various methods of converting the RBG values for each pixel to a single intensity value are described below.

The first method is to use three weights to convert the three color channels into intensity values. In other words, each color channel has a unique weight or conversion factor. You can use a default set of three transform coefficients defined in the system recipe or modify them based on sample measurement requirements. The second method is to subtract the color channel for each pixel from the default color channel value of each color channel and the result is converted to an intensity value using the transform coefficients discussed in the first method. The third method is to convert the color to an intensity value using a "color difference" scheme. In the color difference scheme, the resulting pixel intensity is defined by comparing how close the pixel's color is to the predefined fixed red, green, and blue color values. An example of a color difference is a weight vector distance between a color value of a pixel and a fixed color value. Another method of "color difference" is a color difference method that uses a fixed color value that is automatically derived from an image. In one example, the border area of the image is known as the background color. The weighted average of the colors of the boundary area pixels can be used as a fixed color value for the color difference system.

Once the color image has been converted to the intensity image, the intensity threshold can be compared to the intensity of each pixel to determine the area of the image to which the pixel belongs. In other words, a pixel having an intensity value higher than the intensity threshold indicates that the pixel has received reflected light from the first surface of the sample, and a pixel having an intensity value lower than the intensity threshold, Indicating that it has not received the reflected light. Once each pixel of the image is mapped to a region, the approximate shape of the feature with focus in the image can be determined.

Figures 20, 21 and 22 illustrate an intensity threshold that can be used to distinguish pixels measuring light that is reflected from the top surface of the photoresist layer and pixels that do not reflect light from the top surface of the photoresist layer ≪ / RTI >

Figure 20 illustrates a first method of generating intensity thresholds used to analyze a captured image. In this first method, a count of pixels is generated for each measured intensity value. This type of graph is also called a histogram. Once a count of pixels per intensity value is generated, a range of intensities between the peak count of pixels produced from the measurement light reflected from the photoresist layer and the peak count of pixels generated from the measurement light that is not reflected from the photoresist layer can be determined have. The intensity value within the intensity range is selected as the intensity threshold value. In one example, the midpoint between the two peak counts is chosen to be the critical intensity. Different intensity values between two peak counts may be used in other examples falling within the scope of the present disclosure.

Figure 21 is a second method of generating an intensity threshold used to analyze the captured image. At step 311, a determination is made regarding a first percentage of the captured image representing the photoresist region. This determination can be made according to physical measurements, optical inspection or production specifications. At step 312, a determination is made regarding a second percentage of the captured image representing the photoresist aperture area. This determination can be made according to physical measurements, optical inspection or production specifications. At step 313, all pixels of the captured image are sorted according to the intensity measured by each pixel. In step 314, all pixels with intensity in the lower second percentage of all pixel intensities are selected. At step 315, all selected pixels are analyzed.

Figure 22 shows a third method for determining the intensity threshold. In step 321, a predetermined intensity threshold is stored in memory. In step 322, the intensity of each pixel is compared to the stored intensity threshold. At step 323, all pixels having intensity values less than the intensity threshold are selected. At step 324, the selected pixels are analyzed.

Regardless of how the intensity threshold is generated, the threshold intensity value is used to determine where the boundary of the feature in the captured image is located roughly. The approximate boundary of the feature will be used to determine a much more accurate measurement of the boundary of the features discussed below.

23 is a 3-D diagram of the photoresist aperture shown in Fig. Various photoresist aperture measurements are of interest during the fabrication process, such as the area of the top and bottom openings, the diameter of the top and bottom openings, the circumference of the top and bottom openings, the cross-sectional width of the top and bottom openings, and the depth of the openings. The first measurement is the upper surface opening area. Fig. 8 (and accompanying text) illustrates how the focused image on the top surface of the photoresist aperture and the focused image on the bottom surface of the photoresist aperture are selected from a plurality of images taken at different distances from the sample. Once the focused image is selected on the top surface, the focused image on the top surface of the photoresist opening can be used to determine the above-mentioned top aperture measurement. Likewise, once a focused image is selected on the bottom surface of the photoresist aperture, a focused image on the bottom surface of the photoresist aperture can be used to determine the above-mentioned bottom aperture measurement. U.S. Patent Application No. 12 / 699,824 entitled "3-D Optical Microscope" by James Jianguo Xu et al., Filed on Feb. 3, 2010, the contents of which are incorporated herein by reference A pattern or a grid may be projected onto the surface of the sample while the plurality of images are being captured, as discussed in < RTI ID = 0.0 > U. < / RTI > In one example, a projected pattern or an image comprising a grid is used to determine the photoresist aperture measurement. In another example, a new image that does not include a pattern or grid captured at the same z-distance is used to determine photoresist aperture measurements. In the latter example, a projected pattern on the sample or a new image without a grid provides a "cleaner" image that can more easily detect the boundary of the photoresist opening.

Figure 24 is a 2-D diagram of the top surface opening shown in Figure 23; The 2-D diagram clearly shows the boundary of the upper surface opening (solid line) 40. The boundary is traced using the best fit line (dashed line 41). Once the optimal ray traces are generated, the diameter, area and circumference of the optimum ray 41 can be generated.

25 is a 2-D diagram of the lower surface opening shown in Fig. The 2-D diagram clearly shows the boundary of the lower surface opening (solid line 42). The boundary is traced using the optimal line (dashed line 43). Once the optimal ray traces are generated, the bottom surface opening diameter, area, and circumference of the optimal line can be calculated.

In this example, the optimal line is automatically generated by the computer system in communication with the optical microscope. As discussed in more detail below, an optimal line can be generated by analyzing the transition between the dark and light portions of the selected image.

26 is a 2-D image of the opening of the photoresist layer. The image is focused on the top surface of the photoresist layer. In this example, the light reflected from the top surface of the photoresist layer is bright because the microscope is focused on the top surface of the photoresist layer. The light intensity measured from the photoresist aperture is dark because there is no reflective surface in the photoresist aperture. The intensity of each pixel is used to determine whether the pixel belongs to the top surface of the photoresist or to the opening of the photoresist. The change in intensity from the variation between the top surface of the photoresist and the opening of the photoresist can span multiple pixels and multiple intensity levels. The image background intensity may not be constant. Therefore, further analysis is needed to determine the exact pixel location of the photoresist boundary. To determine the pixel location of a single surface transition point, intensity averages are taken in adjacent bright areas outside the variation area and intensity averages are taken in adjacent dark areas outside the variation area. The intermediate intensity value between the average of the adjacent bright areas and the average of the adjacent dark areas is used as an intensity threshold to distinguish whether the pixel belongs to the top surface of the photoresist or to the aperture of the photoresist. This intensity threshold may differ from the previously discussed intensity threshold used to select a feature within a single captured image. Once the intermediate intensity threshold is determined, the intermediate intensity threshold is compared to all the pixels to distinguish the pixels on the top surface of the photoresist or in the openings of the photoresist. If the pixel intensity is above the intensity threshold, the pixel is determined to be a photoresist pixel. If the pixel intensity is lower than the intensity threshold, the pixel is determined as an aperture area pixel. In this way, it can be used to determine various boundary points and to fit shapes. All the desired dimensions of the top opening of the photoresist are then calculated using the fitted geometry. In one example, the fitted shape may be selected from the group of a circle, a rectangle, a rectangle, a triangle, an oval, a hexagon, a pentagon,

Fig. 27 shows a change in the measured intensity over the neighboring area around the brightness variation of Fig. At the leftmost portion of the neighboring region, the measured intensity is high because the microscope is focused on the top surface of the photoresist layer. The measured light intensity decreases through the brightness variation of the neighboring area. The measured light intensity drops to the minimum extent from the rightmost portion of the neighboring region, because the top surface of the photoresist layer is not present in the rightmost portion of the neighboring region. Figure 27 graphically illustrates this change in intensity measured over the neighboring region. The boundary point indicating the point at which the top surface of the photoresist layer terminates may be determined by application of the critical intensity. The boundary point at which the top surface of the photoresist ends is located at the intersection of the measured intensity and the critical intensity. This process is repeated in different neighboring regions located along the brightness variation. A boundary point is determined for each neighboring region. Then, the size and shape of the upper surface boundary are determined using the boundary points for each neighboring region.

28 is a 2-D image of the opening of the photoresist layer. The image is focused on the bottom surface of the photoresist opening. In this example, the light reflected from the bottom surface of the photoresist aperture region is bright because the microscope is focused on the bottom surface of the photoresist aperture. Since the substrate is a silicon or metal seed layer with a high reflectivity, the light reflected from the photoresist region is also relatively bright. The light reflected from the boundary of the photoresist layer is dark due to light scattering caused by the photoresist boundary. The measured intensity of each pixel is used to determine whether the pixel belongs to the bottom surface of the photoresist aperture or not. The change in intensity from the variation between the bottom surface of the photoresist and the photoresist opening region can span a number of pixels and multiple intensity levels. The image background intensity may not be constant. Additional analysis is therefore required to determine the exact pixel location of the photoresist aperture. To determine the pixel position of the boundary point, the position of the pixel with the minimum intensity is determined in the neighboring pixel. In this way, multiple boundary points can be determined and used to fit geometry. The fitted geometry is used to calculate the desired dimension of the bottom opening.

Figure 29 shows the change in measured intensity over the neighboring area around the brightness variation of Figure 28; At the leftmost portion of the neighboring region, the measured intensity is high because the microscope is focused on the bottom surface of the photoresist opening. The measured light intensity decreases to a minimum intensity and then increases through the brightness variation of the neighboring areas. The measured light intensity rises from the rightmost portion of the neighboring region to a relatively high intensity range due to light reflection from the substrate surface. Figure 29 graphically illustrates this change in intensity measured over the neighboring region. The boundary point indicating where the boundary of the photoresist opening is located can be determined by finding the position of the minimum measurement intensity. The boundary point is at the point where the minimum measurement intensity is located. This process is repeated in different neighborhoods located along the brightness variation. A boundary point is determined for each neighboring region. The boundary points for each neighboring region are then used to determine the size and shape of the bottom surface boundary.

30 is a 2-D image of the trench structure of a photoresist layer, such as an unplated RDL structure. The image is focused on the top surface of the photoresist layer. In this example, the light reflected from the top surface of the photoresist layer is bright because the microscope is focused on the top surface of the photoresist layer. Since the amount of light reflected from the open trench region is less, the light reflected from the opening of the photoresist layer is darker. The intensity of each pixel is used to determine whether the pixel belongs to the top surface of the photoresist or to the aperture region of the photoresist. The change in intensity from the variation between the top surface of the photoresist and the opening area of the photoresist can span multiple pixels and multiple intensity levels. The image background intensity may also not be constant. Therefore, further analysis is needed to determine the exact pixel location of the photoresist boundary. To determine the pixel location of a single surface transition point, intensity averages are taken in adjacent bright areas outside the variation area and intensity averages are taken in adjacent dark areas outside the variation area. The intermediate intensity value between the average of adjacent bright regions and the average of adjacent dark regions is used as the intensity threshold to distinguish the top surface photoresist reflections from the non-top surface photoresist reflections. Once the intermediate intensity threshold is determined, the intermediate intensity threshold is compared to all adjacent pixels to determine the boundary between the top surface pixels and the photoresist aperture region. If the pixel intensity is higher than the intensity threshold, the pixel is determined as the upper surface photoresist pixel. If the pixel intensity is lower than the intensity threshold, the pixel is determined as a photoresist aperture area pixel. In this way, it can be used to determine various boundary points and to fit shapes. The fitted geometry is then used to calculate all desired dimensions of the photoresist opening of the trench, such as the trench width.

31 shows a change in measured intensity over a neighboring area around the brightness transition of Fig. The intensity measured at the leftmost portion of the neighboring region is high because the microscope is focused on the top surface of the photoresist layer. The measured light intensity decreases through the brightness variation of the neighboring area. The measured light intensity falls to the minimum extent in the rightmost portion of the neighboring region because the top surface of the photoresist layer is not present in the rightmost portion of the neighboring region. Figure 31 graphically illustrates this change in intensity measured over the neighboring region. The boundary point indicating the point at which the top surface of the photoresist layer terminates may be determined by application of the critical intensity. The boundary point at which the top surface of the photoresist ends is located at the intersection of the measured intensity and the critical intensity. This process is repeated in different neighboring regions located along the brightness variation. A boundary point is determined for each neighboring region. Then, the size and shape of the upper surface boundary are determined using the boundary points for each neighboring region.

As shown in Figures 26-31, pixel intensity is only one example of pixel characteristics that can be used to distinguish pixels in different regions of an image. For example, the wavelength or color of each pixel may be used to distinguish pixels from different regions of the image in a similar manner. If the boundary between each region is defined correctly, it is used to determine the critical dimension (CD), such as the diameter or width of the PR aperture. Often the measured CD value is compared to the measured value in other types of tools such as critical dimension-scanning electron microscope (CD-SEM). This type of cross-calibration is necessary to ensure measurement accuracy between production monitoring tools.

Although specific embodiments have been described above for purposes of teaching, the teachings of this patent document have general applicability and are not limited to the specific embodiments described above. Accordingly, various modifications, adaptations, and combinations of various features of the described embodiments can be made without departing from the scope of the invention as set forth in the claims.

Claims (21)

A method for generating three-dimensional (3-D) information of a sample using an optical microscope,
Changing the distance between the sample and the objective lens of the optical microscope in predetermined steps;
Capturing an image at each predetermined step, the first surface of the sample and the second surface of the sample being within the field of view of each captured image;
Determining a property value of each pixel in each captured image;
Determining a first captured image to be focused on a first surface of the sample based on a property value of each pixel in each captured image; And
And determining a measure of an aperture of a first surface of the sample based on the first captured image. The method according to claim 1,
Determining a second captured image to be focused on a second surface of the sample based on a characteristic of each pixel in each captured image; And
Further comprising determining a measure of an aperture of a second surface of the sample based on the second captured image. 3. The method of claim 2,
Further comprising determining a distance between a first surface of the sample and a second surface of the sample wherein the distance between the first surface of the sample and the second surface of the sample is indicative of the depth of the opening of the sample Gt; 3-D < / RTI > 2. The method of claim 1, wherein the opening of the first surface of the sample is an opening in the bottom of the hole or trench in the sample. 2. The method of claim 1, wherein the opening of the first surface of the sample is an opening area of a hole or top of the trench in the sample. 2. The method of claim 1, wherein determining the measurement comprises fitting a line at a boundary of an opening of a first surface of the sample shown in the first captured image. -D information generation method. 2. The method of claim 1, wherein the measurement is a diameter of an opening in a first surface of the sample. 2. The method of claim 1, wherein the measurement is the area of the opening of the first surface of the sample. 2. The method of claim 1, wherein the measurement is a width of an aperture of a first surface of the sample. 2. The method of claim 1, wherein the measurement is a shape of an opening in a first surface of the sample. 2. The method of claim 1, wherein the characteristic of each pixel is an intensity. 2. The method of claim 1, wherein the characteristic of each pixel is contrast. 2. The method of claim 1, wherein the characteristic of each pixel is a fringe contrast. 2. The system of claim 1, wherein the optical microscope comprises a stage, wherein the sample is supported by the stage, and wherein the optical microscope is adapted to communicate with a computer system, Wherein the memory device comprises a memory device configured to store the 3-D information. 2. The method according to claim 1, wherein the optical microscope is a confocal microscope. 2. The method of claim 1, wherein the optical microscope is a structured illumination microscope. 2. The method of claim 1, wherein the optical microscope is an interferometric microscope. 2. The method of claim 1, wherein determining a first captured image to be focused on a first surface of the sample comprises:
Determining, for each captured image, a maximum characteristic across all pixels in the captured image; And
Comparing the maximum characteristics for each captured image to determine whether the surface of the sample is present at each predetermined step. ≪ Desc / Clms Page number 20 > 2. The method of claim 1, wherein determining a first captured image to be focused on a first surface of the sample comprises:
Determining, for each captured image, a count of pixels having a property value in a first range, wherein all pixels that do not have property values in the first range are not included in the count of the pixels; And
Further comprising determining whether a surface of the sample is present at each predetermined step based on a count of pixels for each captured image. In a three-dimensional (3-D) measurement system,
An optical microscope comprising an objective lens and a stage and configured to vary a distance between a sample supported by the stage and an objective lens of the optical microscope in predetermined steps; And
A computer system comprising a processor and a storage device,
The computer system comprising:
Storing a captured image at each predetermined step, the first surface of the sample and the second surface of the sample being within the field of view of each image;
Determine a characteristic of each pixel in each captured image;
Determine a first captured image to be focused on a first surface of the sample based on a characteristic of each pixel in each captured image;
And to determine a measure of an aperture of a first surface of the sample based on the first captured image. A method for generating three-dimensional (3-D) information of a sample using an optical microscope,
Changing the distance between the sample and the objective lens of the optical microscope in predetermined steps;
Capturing an image at each predetermined step, wherein a pattern is projected onto the sample while each image is captured, the first surface of the sample and the second surface of the sample are within the field of view of each captured image has exist - ;
Determining a property value of each pixel in each captured image;
Determining a first captured image that is focused on a first surface of the sample based on a property value of each pixel in each captured image, the first captured image being focused on a focal distance; And
Capturing a second image taken at the focal distance, the pattern not being projected onto the sample while the second image is captured; And
And determining a measure of an aperture of a first surface of the sample based on the second captured image.

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